library(ggplot2)
library(viridis)
Loading required package: viridisLite
library(pheatmap)
library(grid)
library(gridExtra)
Directories used:
outdir <- "/mnt/bmh01-rds/UoOxford_David_W/b05055gj/AmplificationTimeR_synthetic_tests/multinomial_sampled_data/final_figures/"
results_dir <- "/mnt/bmh01-rds/UoOxford_David_W/b05055gj/AmplificationTimeR_synthetic_tests/multinomial_sampled_data/"
Experimental parameters:
state_timing_replicates <- 100 # number of replicates for error vs mut number
n_replicates_clocklike <- 100 # number of replicates for error vs clocklike
n_mutations_clocklike_test <- 100 # number of mutations used for clocklike assessment
n_replicates_mismatched <- 100 # number of replicates for timing with different equations
n_mutations_mismatched <- 100 # number of mutations used for timing with different equations
data_type <- "multinomial"
Output figure parameters:
figure_height <- 22.5
figure_width <- 17.8
figure_width_half <- 8.6
resolution <- 350
# Mutation number
multinomial_mutation_number_and_accuracy <- read.delim(paste0(results_dir,"Error_vs_mutation_number/mutation_number_and_error_rate_100_replicates_multinomial_sampled_data_2024-01-07.txt"), sep = "\t", header = TRUE, row.names = 1)
multinomial_mutation_number_and_number_correct_order <- read.delim(paste0(results_dir,"Error_vs_mutation_number/mutation_number_and_correct_order_100_replicates_multinomial_sampled_data_2024-01-07.txt"), sep = "\t", header = TRUE, row.names = 1)
# Clocklike proportion
multinomial_clocklike_proportions_and_accuracy <- read.delim(paste0(results_dir,"Error_vs_proportion_clocklike/clocklike_proportion_and_error_rate_100_replicates100_mutations_multinomial_sampled_data_2024-01-08.txt"), sep = "\t", header = TRUE, row.names = 1)
multinomial_clocklike_proportions_and_number_correct_order <- read.delim(paste0(results_dir,"Error_vs_proportion_clocklike/clocklike_proportion_and_correct_order_100_replicates100_mutations_multinomial_sampled_data_2024-01-08.txt"), sep = "\t", header = TRUE, row.names = 1)
colnames(multinomial_mutation_number_and_accuracy) <- gsub("X","",colnames(multinomial_mutation_number_and_accuracy))
plot_multinomial_mutation_number_and_accuracy <- as.matrix(multinomial_mutation_number_and_accuracy)
rownames(plot_multinomial_mutation_number_and_accuracy) <- rownames(multinomial_mutation_number_and_accuracy)
png(paste0(outdir,"mutation_number_and_error_rate_",
state_timing_replicates,"_replicates",
"_","multinomial_data","_",
Sys.Date(),".png"),
height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
pheatmap(plot_multinomial_mutation_number_and_accuracy,
cluster_rows = FALSE, cluster_cols = FALSE,
# col = viridis(100),
color = c(viridis(100), viridis(100)[100]),
breaks = c(0:100,max(plot_multinomial_mutation_number_and_accuracy)),
main = paste0("Average error rate (%) of timing (n=",state_timing_replicates,") - ","Multinomial Simulated Data"),
xlab = "Number of mutations simulated",
ylab = "Copy number state and order",
border_color = NA,
scale = "none",
fontsize_row = 7)
setHook("grid.newpage", NULL, "replace")
grid.text("Number of mutations simulated", y=-0.02, gp=gpar(fontsize=16))
grid.text("Copy number state and order", x=-0.07, rot=90, gp=gpar(fontsize=16))
dev.off()
png
3
colnames(multinomial_mutation_number_and_number_correct_order) <- gsub("X","",colnames(multinomial_mutation_number_and_number_correct_order))
plot_multinomial_mutation_number_and_number_correct_order <- as.matrix(multinomial_mutation_number_and_number_correct_order)
rownames(plot_multinomial_mutation_number_and_number_correct_order) <- rownames(multinomial_mutation_number_and_number_correct_order)
png(paste0(outdir,
"mutation_number_and_correct_order_",
state_timing_replicates,"_replicates",
"_","multinomial_Data","_",
Sys.Date(),".png"),
height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
pheatmap(plot_multinomial_mutation_number_and_number_correct_order,
cluster_rows = FALSE, cluster_cols = FALSE,
col = viridis(100),
main = paste0("Number of correct timing orders (n=",state_timing_replicates,") - ","Multinomial Simulated Data"),
border_color = NA,
scale = "none",
fontsize_row = 7)
setHook("grid.newpage", NULL, "replace")
grid.text("Number of mutations simulated", y=-0.02, gp=gpar(fontsize=16))
grid.text("Copy number state and order", x=-0.07, rot=90, gp=gpar(fontsize=16))
dev.off()
png
3
colnames(multinomial_clocklike_proportions_and_accuracy) <- gsub("X","",colnames(multinomial_clocklike_proportions_and_accuracy))
plot_multinomial_clocklike_proportions_and_accuracy <- as.matrix(multinomial_clocklike_proportions_and_accuracy)
rownames(plot_multinomial_clocklike_proportions_and_accuracy) <- rownames(multinomial_clocklike_proportions_and_accuracy)
png(paste0(outdir,
"clocklike_proportion_and_error_rate_",
n_replicates_clocklike,"_replicates",
n_mutations_clocklike_test,"_mutations",
"_","multinomial_data","_",
Sys.Date(),".png"),
height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
pheatmap(plot_multinomial_clocklike_proportions_and_accuracy,
cluster_rows = FALSE, cluster_cols = FALSE,
# col = viridis(100),
color = c(viridis(100), viridis(100)[100]),
breaks = c(0:100,max(plot_multinomial_clocklike_proportions_and_accuracy)),
main = paste0("Average error rate (%) of timing (n=",state_timing_replicates,") - ","Multinomial Simulated Data"),
xlab = "Proportion of clocklike mutations simulated",
ylab = "Copy number state and order",
border_color = NA,
scale = "none",
fontsize_row = 7)
setHook("grid.newpage", NULL, "replace")
grid.text("Proportion of clocklike mutations simulated", y=-0.02, gp=gpar(fontsize=16))
grid.text("Copy number state and order", x=-0.07, rot=90, gp=gpar(fontsize=16))
dev.off()
png
3
colnames(multinomial_clocklike_proportions_and_number_correct_order) <- gsub("X","",colnames(multinomial_clocklike_proportions_and_number_correct_order))
plot_multinomial_clocklike_proportions_and_number_correct_order <- as.matrix(multinomial_clocklike_proportions_and_number_correct_order)
rownames(plot_multinomial_clocklike_proportions_and_number_correct_order) <- rownames(multinomial_clocklike_proportions_and_number_correct_order)
png(paste0(outdir,
"clocklike_proportion_and_correct_order_",
n_replicates_clocklike,"_replicates",
n_mutations_clocklike_test,"_mutations",
"_","multinomial_data","_",
Sys.Date(),".png"),
height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
pheatmap(plot_multinomial_clocklike_proportions_and_number_correct_order,
cluster_rows = FALSE, cluster_cols = FALSE,
col = viridis(100),
main = paste0("Number of correct timing orders (n=",state_timing_replicates,") - ","Multinomial Simulated Data"),
border_color = NA,
scale = "none",
fontsize_row = 7)
setHook("grid.newpage", NULL, "replace")
grid.text("Proportion of clocklike mutations simulated", y=-0.02, gp=gpar(fontsize=16))
grid.text("Copy number state and order", x=-0.07, rot=90, gp=gpar(fontsize=16))
dev.off()
png
3
mut_number_accuracy_heatmap <- pheatmap(plot_multinomial_mutation_number_and_accuracy,
cluster_rows = FALSE, cluster_cols = FALSE,
# col = viridis(100),
color = c(viridis(100), viridis(100)[100]),
breaks = c(0:100,max(plot_multinomial_mutation_number_and_accuracy)),
main = paste0("Average error rate (%) of timing \n(n=",state_timing_replicates,")"),
xlab = "Number of mutations simulated",
ylab = "Copy number state and order",
border_color = NA,
scale = "none",
fontsize = 7)
clocklike_accuracy_heatmap <- pheatmap(plot_multinomial_clocklike_proportions_and_accuracy,
cluster_rows = FALSE, cluster_cols = FALSE,
# col = viridis(100),
color = c(viridis(100), viridis(100)[100]),
breaks = c(0:100,max(plot_multinomial_clocklike_proportions_and_accuracy)),
main = paste0("Average error rate (%) of timing \n(n=",state_timing_replicates,")"),
xlab = "Proportion of clocklike mutations simulated",
ylab = "Copy number state and order",
border_color = NA,
scale = "none",
fontsize = 7)
png(paste0(outdir,"mutation_number_and_error_rate_",
state_timing_replicates,"_replicates",
"clocklike_proportion_and_error_rate_",
n_replicates_clocklike,"_replicates",
n_mutations_clocklike_test,"_mutations",
"_","multinomial_data","_",
Sys.Date(),".png"),
height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.95, height=0.95, name="vp", just=c("right","top"))), action="prepend")
grid.arrange(grobs = list(mut_number_accuracy_heatmap[[4]],clocklike_accuracy_heatmap[[4]]),
ncol = 2)
setHook("grid.newpage", NULL, "replace")
grid.text("Number of mutations simulated", y=-0.02, x = 0.22, gp=gpar(fontsize=12))
grid.text("Proportion of clocklike mutations simulated", y=-0.02, x = 0.73, gp=gpar(fontsize=12))
grid.text("Copy number state and order", x=-0.02, rot=90, gp=gpar(fontsize=14))
grid.text("A", x=-0.02, y=0.98, gp=gpar(fontsize=16))
grid.text("B", x=0.47, y=0.98, gp=gpar(fontsize=16))
dev.off()
png
2
mut_number_order_heatmap <- pheatmap(plot_multinomial_mutation_number_and_number_correct_order,
cluster_rows = FALSE, cluster_cols = FALSE,
col = viridis(100),
main = paste0("Number of correct timing orders \n(n=",state_timing_replicates,")"),
border_color = NA,
scale = "none",
fontsize = 7)
clocklike_order_heatmap <- pheatmap(plot_multinomial_clocklike_proportions_and_number_correct_order,
cluster_rows = FALSE, cluster_cols = FALSE,
col = viridis(100),
main = paste0("Number of correct timing orders \n(n=",state_timing_replicates,")"),
border_color = NA,
scale = "none",
fontsize = 7)
png(paste0(outdir,"mutation_number_and_correct_order_",
state_timing_replicates,"_replicates",
"clocklike_proportion_and_correct_order_",
n_replicates_clocklike,"_replicates",
n_mutations_clocklike_test,"_mutations",
"_","multinomial_data","_",
Sys.Date(),".png"),
height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.95, height=0.95, name="vp", just=c("right","top"))), action="prepend")
grid.arrange(grobs = list(mut_number_order_heatmap[[4]],clocklike_order_heatmap[[4]]),
ncol = 2)
setHook("grid.newpage", NULL, "replace")
grid.text("Number of mutations simulated", y=-0.02, x = 0.22, gp=gpar(fontsize=12))
grid.text("Proportion of clocklike mutations simulated", y=-0.02, x = 0.73, gp=gpar(fontsize=12))
grid.text("Copy number state and order", x=-0.02, rot=90, gp=gpar(fontsize=14))
grid.text("A", x=-0.02, y=0.98, gp=gpar(fontsize=16))
grid.text("B", x=0.47, y=0.98, gp=gpar(fontsize=16))
dev.off()
png
2
Try new plotting approach
Read in sample data.
cn_4_0_WGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+0 WGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_0_GW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+0 GW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_1_WGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+1 WGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_1_GW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+1 GW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_2_WGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+2 WGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_2_GW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+2 GW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_x <- rbind(cn_4_0_WGG, cn_4_0_GW,
cn_4_1_WGG, cn_4_1_GW,
cn_4_2_WGG, cn_4_2_GW)
cn_4_x$Significance <- ifelse(cn_4_x$Spearman_adjusted_p < 0.05, "Significant","Not \nSignificant")
cn_4_x_pearson_plot <- ggplot(cn_4_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","\nNot Significant",NA), name = "Adjusted p")+
geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
# scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1),
# na.value = "grey")+
scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
# scale_fill_viridis(option = "magma")+
theme_minimal()+
ylab("Applied timing order")+
xlab("True simulated timing order")
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
cn_4_x_pearson_plot
Warning: Removed 3 rows containing missing values (`geom_text()`).
Read in sample data.
cn_5_0_WGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+0 WGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_0_GWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+0 GWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_1_WGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+1 WGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_1_GWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+1 GWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_2_WGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+2 WGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_2_GWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+2 GWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_x <- rbind(cn_5_0_WGGG, cn_5_0_GWG,
cn_5_1_WGGG, cn_5_1_GWG,
cn_5_2_WGGG, cn_5_2_GWG)
cn_5_x$Significance <- ifelse(cn_5_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")
cn_5_x_pearson_plot <- ggplot(cn_5_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
# scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1),
# na.value = "grey")+
scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
# scale_fill_viridis(option = "magma")+
theme_minimal()+
ylab("Applied timing order")+
xlab("True simulated timing order")
cn_5_x_pearson_plot
Warning: Removed 3 rows containing missing values (`geom_text()`).
Read in sample data.
cn_6_0_WGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+0 WGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_0_GWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+0 GWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_0_GGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+0 GGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_1_WGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+1 WGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_1_GWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+1 GWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_1_GGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+1 GGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_2_WGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+2 WGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_2_GWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+2 GWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_2_GGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+2 GGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_x <- rbind(cn_6_0_WGGGG, cn_6_0_GWGG, cn_6_0_GGW,
cn_6_1_WGGGG, cn_6_1_GWGG, cn_6_1_GGW,
cn_6_2_WGGGG, cn_6_2_GWGG, cn_6_2_GGW)
cn_6_x$Significance <- ifelse(cn_6_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")
cn_6_x_pearson_plot <- ggplot(cn_6_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
# scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1),
# na.value = "grey")+
scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
# scale_fill_viridis(option = "magma")+
theme_minimal()+
ylab("Applied timing order")+
xlab("True simulated timing order")
cn_6_x_pearson_plot
Warning: Removed 12 rows containing missing values (`geom_text()`).
Read in sample data.
cn_7_0_WGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+0 WGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_0_GWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+0 GWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_0_GGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+0 GGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_1_WGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+1 WGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_1_GWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+1 GWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_1_GGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+1 GGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_2_WGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+2 WGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_2_GWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+2 GWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_2_GGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+2 GGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_x <- rbind(cn_7_0_WGGGGG, cn_7_0_GWGGG, cn_7_0_GGWG,
cn_7_1_WGGGGG, cn_7_1_GWGGG, cn_7_1_GGWG,
cn_7_2_WGGGGG, cn_7_2_GWGGG, cn_7_2_GGWG)
cn_7_x$Significance <- ifelse(cn_7_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")
cn_7_x_pearson_plot <- ggplot(cn_7_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
# scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1),
# na.value = "grey")+
scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
# scale_fill_viridis(option = "magma")+
theme_minimal()+
ylab("Applied timing order")+
xlab("True simulated timing order")
cn_7_x_pearson_plot
Warning: Removed 12 rows containing missing values (`geom_text()`).
Read in sample data.
cn_8_0_WGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+0 WGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_0_GWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+0 GWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_0_GGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+0 GGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_0_GGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+0 GGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_1_WGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+1 WGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_1_GWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+1 GWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_1_GGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+1 GGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_1_GGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+1 GGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_2_WGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+2 WGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_2_GWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+2 GWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_2_GGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+2 GGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_2_GGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+2 GGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_x <- rbind(cn_8_0_WGGGGGG, cn_8_0_GWGGGG, cn_8_0_GGWGG, cn_8_0_GGGW,
cn_8_1_WGGGGGG, cn_8_1_GWGGGG, cn_8_1_GGWGG, cn_8_1_GGGW,
cn_8_2_WGGGGGG, cn_8_2_GWGGGG, cn_8_2_GGWGG, cn_8_2_GGGW)
cn_8_x$Significance <- ifelse(cn_8_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")
cn_8_x_pearson_plot <- ggplot(cn_8_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
# scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1),
# na.value = "grey")+
scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
# scale_fill_viridis(option = "magma")+
theme_minimal()+
ylab("Applied timing order")+
xlab("True simulated timing order")
cn_8_x_pearson_plot
Warning: Removed 30 rows containing missing values (`geom_text()`).
Read in sample data.
cn_9_0_WGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+0 WGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_0_GWGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+0 GWGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_0_GGWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+0 GGWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_0_GGGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+0 GGGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_1_WGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+1 WGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_1_GWGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+1 GWGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_1_GGWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+1 GGWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_1_GGGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+1 GGGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_2_WGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+2 WGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_2_GWGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+2 GWGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_2_GGWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+2 GGWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_2_GGGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+2 GGGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_x <- rbind(cn_9_0_WGGGGGGG, cn_9_0_GWGGGGG, cn_9_0_GGWGGG, cn_9_0_GGGWG,
cn_9_1_WGGGGGGG, cn_9_1_GWGGGGG, cn_9_1_GGWGGG, cn_9_1_GGGWG,
cn_9_2_WGGGGGGG, cn_9_2_GWGGGGG, cn_9_2_GGWGGG, cn_9_2_GGGWG)
cn_9_x$Significance <- ifelse(cn_9_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")
cn_9_x_pearson_plot <- ggplot(cn_9_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
# scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1),
# na.value = "grey")+
scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
# scale_fill_viridis(option = "magma")+
theme_minimal()+
ylab("Applied timing order")+
xlab("True simulated timing order")
cn_9_x_pearson_plot
Warning: Removed 30 rows containing missing values (`geom_text()`).
Read in sample data.
cn_10_0_WGGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 WGGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_0_GWGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 GWGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_0_GGWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 GGWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_0_GGGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 GGGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_0_GGGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 GGGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_WGGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 WGGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_GWGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 GWGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_GGWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 GGWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_GGGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 GGGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_GGGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 GGGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_WGGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 WGGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_GWGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 GWGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_GGWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 GGWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_GGGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 GGGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_GGGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 GGGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_x <- rbind(cn_10_0_WGGGGGGGG, cn_10_0_GWGGGGGG, cn_10_0_GGWGGGG, cn_10_0_GGGWGG, cn_10_0_GGGGW,
cn_10_1_WGGGGGGGG, cn_10_1_GWGGGGGG, cn_10_1_GGWGGGG, cn_10_1_GGGWGG, cn_10_1_GGGGW,
cn_10_2_WGGGGGGGG, cn_10_2_GWGGGGGG, cn_10_2_GGWGGGG, cn_10_2_GGGWGG, cn_10_2_GGGGW)
cn_10_x$Significance <- ifelse(cn_10_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")
cn_10_x_pearson_plot <- ggplot(cn_10_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
# scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1),
# na.value = "grey")+
scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
# scale_fill_viridis(option = "magma")+
theme_minimal()+
ylab("Applied timing order")+
xlab("True simulated timing order")
cn_10_x_pearson_plot
Warning: Removed 60 rows containing missing values (`geom_text()`).
png(paste0(outdir,"pearson_and_mix_match_equations_4_9_",
n_replicates_mismatched,"_replicates",
n_mutations_mismatched,"_mutations",
"_","multinomial_data","_",
Sys.Date(),".png"),
height = figure_height, width = figure_width, units = "cm",
res = 350)
grid.arrange(cn_4_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0.2), "cm"), legend.position = "left", strip.text = element_text(size = 8), legend.box.spacing = unit(0.05, "cm"), legend.key.width = unit(0.3,"cm"), legend.text = element_text(size = 6), legend.title = element_text(size = 6), legend.box = "horizontal", legend.spacing = unit(0,"cm")),
cn_5_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8)),
cn_6_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8), axis.text.x = element_text(angle = -20)),
cn_7_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8), axis.text.x = element_text(angle = -20)),
cn_8_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8), axis.text.x = element_text(angle = -25)),
cn_9_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8), axis.text.x = element_text(angle = -25)),
# cn_10_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 4), plot.margin = unit(c(0,0,0,0), "cm")),
ncol = 2, nrow = 3,
left = "Applied timing order", bottom = "True simulated timing order",
layout_matrix = rbind(c(1,2),
c(3,4),
c(5,6)
# c(7,7)
),
heights = c(3,5,9), padding = unit(0.5, "cm"))
Warning: Removed 3 rows containing missing values (`geom_text()`).
Removed 3 rows containing missing values (`geom_text()`).
Warning: Removed 12 rows containing missing values (`geom_text()`).
Removed 12 rows containing missing values (`geom_text()`).
Warning: Removed 30 rows containing missing values (`geom_text()`).
Removed 30 rows containing missing values (`geom_text()`).
grid.text("A", x=0.05, y=0.98, gp=gpar(fontsize=16))
grid.text("B", x=0.50, y=0.98, gp=gpar(fontsize=16))
grid.text("C", x=0.05, y=0.82, gp=gpar(fontsize=16))
grid.text("D", x=0.50, y=0.82, gp=gpar(fontsize=16))
grid.text("E", x=0.05, y=0.54, gp=gpar(fontsize=16))
grid.text("F", x=0.50, y=0.54, gp=gpar(fontsize=16))
dev.off()
png
2
png(paste0(outdir,"pearson_and_mix_match_equations_10_X_",
n_replicates_mismatched,"_replicates",
n_mutations_mismatched,"_mutations",
"_","multinomial_data","_",
Sys.Date(),".png"),
height = 16, width = figure_width, units = "cm",
res = 350)
cn_10_x_pearson_plot+
theme(axis.text = element_text(size = 8),
axis.text.x = element_text(angle = -20, hjust = -0.01))
Warning: Removed 60 rows containing missing values (`geom_text()`).
dev.off()
png
2
sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /opt/apps/apps/gcc/R/4.2.2/lib64/R/lib/libRblas.so
LAPACK: /opt/apps/apps/gcc/R/4.2.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gridExtra_2.3 pheatmap_1.0.12 viridis_0.6.4 viridisLite_0.4.2
[5] ggplot2_3.4.4
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 bslib_0.4.1 compiler_4.2.2 pillar_1.9.0
[5] jquerylib_0.1.4 tools_4.2.2 digest_0.6.33 jsonlite_1.8.7
[9] evaluate_0.22 lifecycle_1.0.3 tibble_3.2.1 gtable_0.3.4
[13] pkgconfig_2.0.3 rlang_1.1.1 cli_3.6.1 rstudioapi_0.15.0
[17] yaml_2.3.7 xfun_0.40 fastmap_1.1.0 withr_2.5.1
[21] dplyr_1.1.3 knitr_1.44 generics_0.1.3 vctrs_0.6.4
[25] sass_0.4.4 tidyselect_1.2.0 glue_1.6.2 R6_2.5.1
[29] fansi_1.0.5 rmarkdown_2.25 farver_2.1.1 magrittr_2.0.3
[33] scales_1.2.1 htmltools_0.5.3 colorspace_2.1-0 labeling_0.4.3
[37] utf8_1.2.4 munsell_0.5.0 cachem_1.0.6 crayon_1.5.2
Save session info
writeLines(capture.output(sessionInfo()), paste0(outdir,data_type,"_plotting_simulation_sessionInfo_",Sys.Date(),".txt"))